Battery hysteresis modeling for state of charge estimation based on Extended Kalman Filter

Qiu, Shiqi; Chen, Zhihang; Masrur, M. Abul; Murphey, Yi Lu

doi:10.1109/iciea.2011.5975576

Cited by 23 publications

(9 citation statements)

References 8 publications

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“…EMF and the other model parameters can be calculated by the updated SOC. After that, the battery voltage can be estimated using (10) and (11) point-by-point per sampling interval…”

Section: Model Parameter Verificationmentioning

confidence: 99%

See 1 more Smart Citation

Electric circuit modelling for lithium‐ion batteries by intermittent discharging

Hsieh¹,

Lin²,

Chen³

et al. 2014

IET Power Electronics

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This study introduces a battery modelling technique for lithium-ion batteries. Repeated intermittent discharging experiments are performed on batteries for a lot of times, and followed by analyses on the battery terminal voltage curves to find an appropriate battery model based on circuit elements and the equations of the battery model parameters. The proposed model is verified by simulating the battery voltages under discharging with different currents. Dynamic behaviours of the battery can be predicted by this model. It can be expected furthermore, real-time state-of-charge estimation algorithm maybe developed by using this model.

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“…EMF and the other model parameters can be calculated by the updated SOC. After that, the battery voltage can be estimated using (10) and (11) point-by-point per sampling interval…”

Section: Model Parameter Verificationmentioning

confidence: 99%

“…With accurate battery models, it is possible to tender the precise battery features for the simulations of battery power system. Moreover, it is also possible to speculate SOC adversely or SOH by examining the battery modelling component parameter [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Electric circuit modelling for lithium‐ion batteries by intermittent discharging

Hsieh¹,

Lin²,

Chen³

et al. 2014

IET Power Electronics

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“…Between all the different OCV(SOC) models identified in literature (e.g. [6]- [10]), the one presented in [11] was used as the basis for the design of an enhanced OCV model in this research work. The developed approach consists of an empirical model that improved in terms of precision considers all the quoted OCV influential factors.…”

Section: Ocv(soc) Modelmentioning

confidence: 99%

Proposal and Validation of a SOC Estimation Algorithm of LiFePO4 Battery Packs for Traction Applications

Garmendia¹,

Pérez²,

Viscarret³

et al. 2013

WEVJ

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An accurate onboard State-of-Charge (SOC) estimation is one of the key functions a Battery Management System (BMS) has to perform in order to provide the optimal performance management of the battery system under control.In this framework, this paper presents a proposal of an Enhanced Coulomb Counting (CC) State-of-Charge estimation algorithm based on Constant Voltage Charge Detection (CVCD) and Open Circuit Voltage (OCV) model for LiFePO 4 batteries. Designed for onboard BMS implementation, it is characterized by its simplicity and operability in wide operating conditions (under diverse load profiles, temperatures, SOC ranges, etc.). The description of the algorithm at both, cell and battery-module level is detailed in the paper.Furthermore, its on-line experimental validation and scope determination is tested under three different traction applications and cell specimens in an own-developed real time validation platform: 2.5 Ah cells (Type A) in a residential elevator application, 8 Ah cells (Type B) in a pure electric on-road vehicle application and 100 Ah cells (Type C) in an electric railway vehicle application. According to the achieved results, the accuracy and versatility of the algorithm for different operating scenarios is certainly proven. In the worst case scenario the algorithm is capable of keeping the SOC estimation of the system under test stabilized around 5% of error.

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“…Many battery models describing hysteresis in terms of terminal variables have been proposed in [6]- [10]. Among them, the most widely used is the one-state model well described in [9], whose circuit interpretation is given in [10].…”

Section: Introductionmentioning

confidence: 99%

Hysteresis Modeling in Li-Ion Batteries

et al. 2014

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This paper investigates the applicability of the scalar Preisach model to describe the hysteresis in the state of charge versus open-circuit voltage plane of lithium-iron-phosphate batteries. The model is implemented using the Everett function, identified with experimental data related to first-order reversal hysteresis branches. To show the effectiveness of the approach and the predictive capabilities of the Preisach model in this peculiar application, numerical simulations of a major hysteresis loop and a set of minor loops are compared with the experimental data

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Battery hysteresis modeling for state of charge estimation based on Extended Kalman Filter

Cited by 23 publications

References 8 publications

Electric circuit modelling for lithium‐ion batteries by intermittent discharging

Electric circuit modelling for lithium‐ion batteries by intermittent discharging

Proposal and Validation of a SOC Estimation Algorithm of LiFePO4 Battery Packs for Traction Applications

Hysteresis Modeling in Li-Ion Batteries

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